The main problem these days is information is generated much more quickly than it can be classified. Short of forcing everyone in the world to adopt a standard for semantic markup for everything they publish, which will almost certainly never happen, the alternative is to use algorithms to extract and classify entities and relationships in unstructured data.
Logistically it would be near impossible to manually vet every single automatically classified piece of data, but it would be possible to assume a % accuracy based on known training sets representing a realistic cross section of the type of data being classified. Once the data is categorised and related across numerous taxonomy, within a certain degree of error, a great deal of useful actions are possible.
Say you want to fetch all opinion pieces where the subject is a certain political party, then run a sentiment analysis to see if they are largely favourable or unfavourable, then drill down to see what aspects cause opinion to sway (could it be the gender of the author, the age, the country) etc. Even with a certain degree of error, this kind of analysis would be very valuable for strategists, reporters, researchers, etc.
The same could be done for stock markets with companies, shareholders and traders all being able to delve into the masses of data being poured daily into the web. Through network analysis (traversing the knowledge graph) you'd be able to identify clusters of similar information, to spot a smear campaign or a sudden surge of bot-generated "fake news" in the making or otherwise identify risks and opportunities in near real-time. To be able to do this, you need some way of extracting meaning from random blobs of text.
There's already a raft of services out there that offer some form of knowledge extraction and sentiment analysis - improving the accuracy of the underlying algorithms gives a competitive edge and makes the services more valuable over time. This includes developing AI that can interpret the higher level thesis with reasonable accuracy (keep in mind many humans fail miserably at this too) and how to avoid purposeful attempts to fool or spam the system. These are all problems that can be tackled as data research continues.
Even for a researcher in any field, being able to upload your thesis, have it analysed and classified, then using the metadata to explore similar research would be advantageous. You may find connections between unlikely fields of research that might have been overlooked with standard keyword relevancy searches, which could go on to spawn new areas to study or new considerations for existing research. Quite often advancements come when disparate fields are bridged, and having tools to help automate the process (even if they aren't 100% accurate) would be very useful.
I doubt we're close to replacing the human element just yet, but having at our disposal better tools to sift through the petabytes of crap on the internet is a highly sought after and valuable outcome, and much more than an intellectual exercise.